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Found 4 papers, 1 papers with code
One-Shot Federated Learning with Classifier-Guided Diffusion Models
Leveraging the extensive knowledge stored in the pre-trained diffusion model, the synthetic datasets can assist us in surpassing the knowledge limitations of the client samples, resulting in aggregation models that even outperform the performance ceiling of centralized training in some cases, which is convincingly demonstrated in the sufficient quantification and visualization experiments conducted on three large-scale multi-domain image datasets.
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Specifically, we first extract prototypes from the labeled data on the server and send them to the clients.
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for multi-domain collaborative image classification with powerful foundation models, like CLIP.
Cross-domain Federated Object Detection
Federated learning can enable multi-party collaborative learning without leaking client data.
